| from pytorch_lightning import Trainer |
| from pytorch_lightning.callbacks import ModelCheckpoint |
| from pytorch_lightning.strategies import DDPStrategy |
| from pytorch_lightning.loggers import WandbLogger |
| from torch import nn |
|
|
| from asr.data_module import ASRDataModule |
| from asr.meldataset import build_dataloader |
| from utils import * |
| from asr.models import build_model |
| from asr.trainer import ASRTrainer |
|
|
| import os |
| import os.path as osp |
| import yaml |
| import shutil |
| import click |
|
|
| def get_data_path_list(train_path=None, val_path=None): |
| if train_path is None: |
| train_path = "Data/train_list.txt" |
| if val_path is None: |
| val_path = "Data/val_list.txt" |
|
|
| with open(train_path, 'r') as f: |
| train_list = f.readlines() |
| with open(val_path, 'r') as f: |
| val_list = f.readlines() |
|
|
| return train_list, val_list |
| def build_criterion(critic_params={}): |
| criterion = { |
| "ce": nn.CrossEntropyLoss(ignore_index=-1), |
| "ctc": torch.nn.CTCLoss(**critic_params.get('ctc', {})), |
| } |
| return criterion |
|
|
| @click.command() |
| @click.option('-c', '--config_path', default='configs/asr.yml', type=str) |
| def main(config_path): |
| config = yaml.safe_load(open(config_path)) |
| log_dir = config['log_dir'] |
| os.makedirs(log_dir,exist_ok=True) |
| shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path))) |
|
|
|
|
| batch_size = config.get('batch_size', 10) |
| epochs = config.get('epochs', 1000) |
| save_freq = config.get('save_freq', 20) |
| train_path = config.get('train_data', None) |
| val_path = config.get('val_data', None) |
|
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| |
| data_module = ASRDataModule(data_dir='dump', batch_size=batch_size, num_workers=8) |
|
|
| model = build_model(model_params=config['model_params'] or {}) |
|
|
| checkpoint_callback = ModelCheckpoint( |
| dirpath=log_dir, |
| filename=('{epoch}-{step}'), |
| every_n_train_steps=None, |
| every_n_epochs=1, |
| verbose=True, |
| save_last=True |
| ) |
| logger = WandbLogger(project="asr-align") |
|
|
| blank_index = 0 |
|
|
| criterion = build_criterion(critic_params={ |
| 'ctc': {'blank': blank_index}, |
| }) |
| training_wrapper = ASRTrainer(model=model, criterion=criterion,mono_start_epoch=1,lr=1e-4) |
| if config.get('pretrained_model',None): |
| training_wrapper.load_checkpoint(config['pretrained_model']) |
|
|
| trainer: Trainer = Trainer( |
| max_epochs=epochs, |
| accelerator='gpu', |
| devices=-1, |
| benchmark=False, |
| fast_dev_run=False, |
| strategy=DDPStrategy(find_unused_parameters=False), |
| logger=logger, |
| precision='16-mixed', |
| callbacks=[checkpoint_callback]) |
|
|
| trainer.fit(training_wrapper, data_module, ckpt_path='logs/asr/epoch=12-step=54140.ckpt') |
|
|
|
|
| if __name__ == "__main__": |
| main() |
